Topographical models of geographical areas may be used for many applications. For example, topographical models may be used in flight simulators and for planning military missions. Furthermore, topographical models of man-made structures (e.g., cities) may be extremely helpful in applications such as cellular antenna placement, urban planning, disaster preparedness and analysis, and mapping, for example.
Various types of and methods for making topographical models are presently being used. One common topographical model is the digital elevation model (DEM). A DEM is a sampled matrix representation of a geographical area which may be generated in an automated fashion by a computer. In a DEN, coordinate points are made to correspond with a height value.
Conventional DEMs are typically used for modeling terrain where the transitions between different elevations (e.g., valleys, mountains, etc.) are generally smooth from one to a next. That is, DEMs typically model terrain at spacings of 0-30 meters presently and as a plurality of curved surfaces and any discontinuities therebetween are thus “smoothed” over. Thus, in a typical DEM no distinct objects are present on the terrain.
One particularly advantageous 3D site modeling product is RealSite® from the present Assignee Harris Corp. RealSite® may be used to register overlapping images of a geographical area of interest, and extract high resolution DEMs using stereo and nadir view techniques. RealSite® provides a semi-automated process for making three-dimensional (3D) topographical models of geographical areas, including cities, that have accurate textures and structure boundaries. Moreover, RealSite® models are geospatially accurate. That is, the location of any given point within the model corresponds to an actual location in the geographical area with very high accuracy. The data used to generate RealSite® models may include aerial and satellite photography, electro-optical, infrared, and Light Detection and Ranging (LIDAR), for example.
Another advantageous approach for generating 3D site models is set forth in U.S. Pat. No. 6,654,690 to Rahmes et al., which is also assigned to the present Assignee and is hereby incorporated herein in its entirety by reference. This patent discloses an automated method for making a topographical model of an area including terrain and buildings thereon based upon randomly spaced data of elevation versus position. The method includes processing the randomly spaced data to generate gridded data of elevation versus position conforming to a predetermined position grid, processing the gridded data to distinguish building data from terrain data, and performing polygon extraction for the building data to make the topographical model of the area including terrain and buildings thereon.
One potentially challenging aspect of generating geospatial models such as DEMS is that high resolutions (i.e., data point or post spacing of ≦1 m) are becoming the norm for terrain representation. As the density of data points in high resolution DEMS (HRDEMs) increases, so too does the volume of data generated for such models. The size of these models can be extremely burdensome to even the most powerful geospatial data processing computers in some applications.
Various approaches are sometimes used to decrease the size of a data field, such as HRDEM data. One approach is to increase the frequency of sampling. However, this may result in other problems requiring labor and time intensive manual editing. Various automated approaches have also been proposed for DEM data thinning, such as simple decimation (e.g., every 5th post is selected), local operators (e.g., a 3×3 filter), and global greedy insertion methods. Yet, these approaches may have certain drawbacks as well. For example, simple decimation of points with DEMs frequently creates artifacts by missing tops and bottoms of geographic features. Moreover, local operators alone can miss areas where gradual changes in height accumulate to a significant point. Additionally, traditional global greedy insertion methods are typically computer-intensive and may not provide desired performance on LIDAR data surveys, for example.